The strategy of negative politeness in the judicial discourse of the USA and Canada (based on the proceedings of the Supreme Court of the USA and the Supreme Court of Canada)
Bibliographic record
Abstract
The purpose of the study is to identify the linguistic specifics of the implementation of a negative politeness strategy in the judicial discourse of the United States and Canada. The research is carried out in line with pragmatic and sociolinguistic approaches to the analysis of discourse. The article examines the linguistic means of English at different levels, which constitute the characteristic features of negative politeness in Western culture as a means of mitigating threats to the addressee’s face. The author analyzes the polite formulas for expressing requests, apologies and gratitude in terms of qualitative and quantitative differences in the form of treatment. The scientific novelty is due to the application of a discursive approach to the study of linguistic features of the implementation of the politeness strategy in American and Canadian judicial discourse, which allowed us to obtain new data on the material of direct speech interaction. As a result of the study, it was found that the implementation of negative politeness in the American and Canadian versions of judicial discourse is characterized by the use of different status forms of addressing judges and lawyers, a higher degree of frequency of using requests without mitigating the threat to a person in the speech of judges of the Supreme Court of the United States.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".